Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models
In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX (''X'' stands for exogenous/fundamental variable - system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-of-sample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting.
Year of publication: |
2006
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Authors: | Adam, Misiorek ; Stefan, Trueck ; Rafal, Weron |
Published in: |
Studies in Nonlinear Dynamics & Econometrics. - De Gruyter, ISSN 1558-3708. - Vol. 10.2006, 3, p. 1-36
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Publisher: |
De Gruyter |
Saved in:
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